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Data and methods

Data, and the methods used to analyze them, are the foundation for evidence-based research. Articles in this subject area discuss the value of different types of data collection, and explain important statistical and econometric methods that provide ways to summarize and present information, and to identify and quantify correlation or causality.

Measures of intergenerational persistence can be
indicative of equality of opportunity, but the relationship is not
clear-cut

A strong association between incomes across
generations—with children from poor families likely to be poor as adults—is
frequently considered an indicator of insufficient equality of opportunity.
Studies of such “intergenerational persistence,” or lack of
intergenerational mobility, measure the strength of the relationship between
parents’ socio-economic status and that of their children as adults.
However, the association between equality of opportunity and common measures
of intergenerational persistence is not as clear-cut as is often assumed. To
aid interpretation researchers often compare measures across time and space
but must recognize that reliable measurement requires overcoming important
data and methodological difficulties.

Studies of independent contractors suggest that
workers’ effort may be more responsive to wage incentives than previously
thought

A fundamental question in economic policy is how
labor supply responds to changes in remuneration. The responsiveness of
labor supply determines the size of the employment impact and efficiency
loss of progressive income taxation. It also affects predictions about the
impacts of policies ranging from fiscal responses to business cycles to
government transfer programs. The characteristics of jobs held by
independent contractors provide an opportunity to overcome problems faced by
earlier studies and help answer this fundamental question.

New sources of data create challenges that may require new skills

Big Data refers to data sets of much larger size, higher frequency, and often more personalized information. Examples include data collected by smart sensors in homes or aggregation of tweets on Twitter. In small data sets, traditional econometric methods tend to outperform more complex techniques. In large data sets, however, machine learning methods shine. New analytic approaches are needed to make the most of Big Data in economics. Researchers and policymakers should thus pay close attention to recent developments in machine learning techniques if they want to fully take advantage of these new sources of Big Data.

Should statistical criteria for measuring employment and
unemployment be re-examined?

Measuring employment and unemployment is essential for economic
policy. Internationally agreed measures (e.g. headcount employment and unemployment rates
based on standard definitions) enhance comparability across time and space, but changes in
real labor markets and policy agendas challenge these traditional conventions. Boundaries
between different labor market states are blurred, complicating identification. Individual
experiences in each state may vary considerably, highlighting the importance of how each
employed or unemployed person is weighted in statistical indices.

More important than defining and measuring
informality is focusing on reducing its detrimental consequences

There are more informal workers than formal
workers across the globe, and yet there remains confusion as to what makes
workers or firms informal and how to measure the extent of it. Informal work
and informal economic activities imply large efficiency and welfare losses,
in terms of low productivity, low earnings, sub-standard working conditions,
and lack of social insurance coverage. Rather than quibbling over
definitions and measures of informality, it is crucial for policymakers to
address these correlates of informality in order to mitigate the negative
efficiency and welfare effects.

Are experiments the gold standard or just
over-hyped?

Non-experimental evaluations of programs compare
individuals who choose to participate in a program to individuals who do
not. Such comparisons run the risk of conflating non-random selection into
the program with its causal effects. By randomly assigning individuals to
participate in the program or not, experimental evaluations remove the
potential for non-random selection to bias comparisons of participants and
non-participants. In so doing, they provide compelling causal evidence of
program effects. At the same time, experiments are not a panacea, and
require careful design and interpretation.

Unlike most OECD countries, Israel experienced a
major increase in both employment and participation rates over the last 15
years

Following a decline in employment and
participation rates during the 1980s and 1990s, Israel managed to reverse
these trends during the last 15 years. This was accompanied by a substantial
decrease in unemployment. New labor force participants are mostly from the
low end of the education distribution, and many are relatively old. They
entered the labor force in response to cuts in welfare payments and
increases in the mandatory retirement age. Net household income for all
population groups has increased due to growth in labor income; however,
inequality between households has increased.

Is there a reproducibility crisis in labor
economics?

There is growing concern that much of the
empirical research in labor economics and other applied areas may not be
reproducible. Correspondingly, recent years have seen an increase in
replication studies published in economics journals. Despite this increase,
there are many unresolved issues about how replications should be done, and
how to interpret their results. Replications have demonstrated a potential
for clarifying the reliability and robustness of previous research. Much can
be done to encourage more replication research, and to exploit the
scientific value of existing replication studies.

There is potential value from incorporating
genetic data in the design of effective public policy, but also some
risks

Both the availability and sheer volume of data
sets containing individual molecular genetic information are growing at a
rapid pace. Many argue that these data can facilitate the identification of
genes underlying important socio-economic outcomes, such as educational
attainment and fertility. Opponents often counter that the benefits are as
yet unclear, and that the threat to individual privacy is a serious one. The
initial exploration presented herein suggests that significant benefits to
the understanding of socio-economic outcomes and the design of both social
and education policy may be gained by effectively and safely utilizing
genetic data.

Why do different population groups (e.g. rural
vs. urban, youth vs. elderly and men vs. women) experience the same
objective labor status differently? One hypothesis is that people are more
concerned with relative deprivation than objective deprivation and they
value their own status relative to the status of their peers—the reference
group. One way to test this hypothesis in the labor market is to measure
individual differences in labor status while controlling for characteristics
that define population groups. This measure is called “relative labor
deprivation” and can help policymakers to better understand how labor claims
are generated.